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environments. Leverage cloud object storage (e.g., Amazon S3) for data staging and artifacts; implement parallel, secure data movement and lifecycle policies. Basic Qualifications: Ph.D. in Computer Science
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algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient partitioning for parallel/distributed AI/ML Optimization of process-to-process communication in parallel/distributed AI/ML
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cell biology, microfluidics and microscopy. Previous experience in one of these fields will be appreciated, good experimental skills in general and a taste for meticulousness are essential. In parallel
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skills in general and a taste for meticulousness are essential. In parallel, interest for soft matter concepts and modelling will be of help to interpret the data. Experience in data analysis (including
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large-scale power system simulation and model reduction techniques. • Hands-on experience with EMT/Phasor-domain-based power system transient simulator development. • Knowledge of parallel and
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and optimization strategies for large-scale or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency. Build and maintain robust data
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learning algorithms for engineering systems Programming experience in FORTRAN, C, or C++ and scripting experience in Python or similar languages Experience with parallel computing environments and Linux
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, for example: Large‑scale optimization and machine learning: Stochastic and/or (non‑)convex optimization methods, first‑order methods, variance reduction, distributed and parallel optimization, federated
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interest in research focused on the valorization of such by-products. In parallel, consumer demand for clean-label and functional foods, together with increasing restrictions on synthetic additives, has
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to significant losses during processing, transport, retail and storage stages. In parallel, advances in sensor, information and communication technologies, together with increased computational capabilities, have